{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# LOL胜利因素分析\n",
    "by Romi\n",
    "\n",
    "**数据字典**\n",
    "\n",
    "项目    | Value\n",
    "-------- | -----\n",
    "teamid  | 比赛ID\n",
    "win  | 是否胜利\n",
    "firstBlood  | 是否取得一血\n",
    "firstTower  | 是否取得一塔\n",
    "firstInhibitor  | 是否率先推掉水晶\n",
    "firstBaron  | 是否拿到第一条大龙\n",
    "firstDragon  | 是否拿到第一条小龙\n",
    "firstRiftHerald  | 是否拿到第一条峡谷先锋\n",
    "towerKills  | 推塔数\t\n",
    "inhibitorKills  | 摧毁水晶数\n",
    "baronKills  | 击杀大龙数\n",
    "dragonKills  | 击杀小龙数\n",
    "riftHeraldKills  | 击杀峡谷先锋数\n",
    "region  | 地区"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# EDA部分"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\kevin.shu\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\statsmodels\\tools\\_testing.py:19: FutureWarning: pandas.util.testing is deprecated. Use the functions in the public API at pandas.testing instead.\n",
      "  import pandas.util.testing as tm\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "success\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "print ('success')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>teamid</th>\n",
       "      <th>win</th>\n",
       "      <th>firstBlood</th>\n",
       "      <th>firstTower</th>\n",
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       "      <th>baronKills</th>\n",
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       "      <td>eun1</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "<p>9226 rows × 14 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      teamid  win  firstBlood  firstTower  firstInhibitor  firstBaron  \\\n",
       "0          0    0           0           0               0           0   \n",
       "1          1    1           1           1               0           0   \n",
       "2          2    0           0           0               0           0   \n",
       "3          3    1           1           1               1           0   \n",
       "4          4    1           1           1               1           0   \n",
       "...      ...  ...         ...         ...             ...         ...   \n",
       "9221    9221    0           0           0               0           0   \n",
       "9222    9222    0           0           1               0           0   \n",
       "9223    9223    1           1           0               1           1   \n",
       "9224    9224    1           1           1               1           1   \n",
       "9225    9225    0           0           0               0           0   \n",
       "\n",
       "      firstDragon  firstRiftHerald  towerKills  inhibitorKills  baronKills  \\\n",
       "0               0                0           0               0           0   \n",
       "1               1                0           1               0           0   \n",
       "2               0                0           0               0           0   \n",
       "3               1                1           8               1           0   \n",
       "4               1                0          10               2           0   \n",
       "...           ...              ...         ...             ...         ...   \n",
       "9221            0                0           0               0           0   \n",
       "9222            0                0           2               0           0   \n",
       "9223            1                1           7               1           1   \n",
       "9224            1                0           9               1           2   \n",
       "9225            0                1           4               0           0   \n",
       "\n",
       "      dragonKills  riftHeraldKills region  \n",
       "0               0                0    na1  \n",
       "1               2                0    na1  \n",
       "2               0                0    na1  \n",
       "3               2                1    na1  \n",
       "4               1                0    na1  \n",
       "...           ...              ...    ...  \n",
       "9221            0                0   eun1  \n",
       "9222            1                0   eun1  \n",
       "9223            3                1   eun1  \n",
       "9224            4                0   eun1  \n",
       "9225            1                1   eun1  \n",
       "\n",
       "[9226 rows x 14 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "match_df=pd.read_csv(r\"matches.csv\")#读取数据文件\n",
    "match_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>teamid</th>\n",
       "      <th>win</th>\n",
       "      <th>firstBlood</th>\n",
       "      <th>firstTower</th>\n",
       "      <th>firstInhibitor</th>\n",
       "      <th>firstBaron</th>\n",
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       "      <th>baronKills</th>\n",
       "      <th>dragonKills</th>\n",
       "      <th>riftHeraldKills</th>\n",
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       "  <tbody>\n",
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       "      <th>count</th>\n",
       "      <td>9226.000000</td>\n",
       "      <td>9226.000000</td>\n",
       "      <td>9226.000000</td>\n",
       "      <td>9226.000000</td>\n",
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       "      <td>9226.000000</td>\n",
       "      <td>9226.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>4612.500000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.499566</td>\n",
       "      <td>0.496206</td>\n",
       "      <td>0.388142</td>\n",
       "      <td>0.27726</td>\n",
       "      <td>0.439627</td>\n",
       "      <td>0.439302</td>\n",
       "      <td>4.611207</td>\n",
       "      <td>0.684587</td>\n",
       "      <td>0.363971</td>\n",
       "      <td>1.554195</td>\n",
       "      <td>0.700087</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>2663.461125</td>\n",
       "      <td>0.500027</td>\n",
       "      <td>0.500027</td>\n",
       "      <td>0.500013</td>\n",
       "      <td>0.487354</td>\n",
       "      <td>0.44767</td>\n",
       "      <td>0.496369</td>\n",
       "      <td>0.496329</td>\n",
       "      <td>3.413101</td>\n",
       "      <td>0.957761</td>\n",
       "      <td>0.583586</td>\n",
       "      <td>1.340606</td>\n",
       "      <td>0.738059</td>\n",
       "    </tr>\n",
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       "      <td>0.000000</td>\n",
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       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>2306.250000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
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       "      <td>0.500000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
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       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
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       "      <th>75%</th>\n",
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       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.00000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.000000</td>\n",
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       "    <tr>\n",
       "      <th>max</th>\n",
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       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.00000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>11.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>2.000000</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            teamid          win   firstBlood   firstTower  firstInhibitor  \\\n",
       "count  9226.000000  9226.000000  9226.000000  9226.000000     9226.000000   \n",
       "mean   4612.500000     0.500000     0.499566     0.496206        0.388142   \n",
       "std    2663.461125     0.500027     0.500027     0.500013        0.487354   \n",
       "min       0.000000     0.000000     0.000000     0.000000        0.000000   \n",
       "25%    2306.250000     0.000000     0.000000     0.000000        0.000000   \n",
       "50%    4612.500000     0.500000     0.000000     0.000000        0.000000   \n",
       "75%    6918.750000     1.000000     1.000000     1.000000        1.000000   \n",
       "max    9225.000000     1.000000     1.000000     1.000000        1.000000   \n",
       "\n",
       "       firstBaron  firstDragon  firstRiftHerald   towerKills  inhibitorKills  \\\n",
       "count  9226.00000  9226.000000      9226.000000  9226.000000     9226.000000   \n",
       "mean      0.27726     0.439627         0.439302     4.611207        0.684587   \n",
       "std       0.44767     0.496369         0.496329     3.413101        0.957761   \n",
       "min       0.00000     0.000000         0.000000     0.000000        0.000000   \n",
       "25%       0.00000     0.000000         0.000000     2.000000        0.000000   \n",
       "50%       0.00000     0.000000         0.000000     4.000000        0.000000   \n",
       "75%       1.00000     1.000000         1.000000     8.000000        1.000000   \n",
       "max       1.00000     1.000000         1.000000    11.000000        7.000000   \n",
       "\n",
       "        baronKills  dragonKills  riftHeraldKills  \n",
       "count  9226.000000  9226.000000      9226.000000  \n",
       "mean      0.363971     1.554195         0.700087  \n",
       "std       0.583586     1.340606         0.738059  \n",
       "min       0.000000     0.000000         0.000000  \n",
       "25%       0.000000     0.000000         0.000000  \n",
       "50%       0.000000     1.000000         1.000000  \n",
       "75%       1.000000     3.000000         1.000000  \n",
       "max       3.000000     5.000000         2.000000  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "match_df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 9226 entries, 0 to 9225\n",
      "Data columns (total 14 columns):\n",
      " #   Column           Non-Null Count  Dtype \n",
      "---  ------           --------------  ----- \n",
      " 0   teamid           9226 non-null   int64 \n",
      " 1   win              9226 non-null   int64 \n",
      " 2   firstBlood       9226 non-null   int64 \n",
      " 3   firstTower       9226 non-null   int64 \n",
      " 4   firstInhibitor   9226 non-null   int64 \n",
      " 5   firstBaron       9226 non-null   int64 \n",
      " 6   firstDragon      9226 non-null   int64 \n",
      " 7   firstRiftHerald  9226 non-null   int64 \n",
      " 8   towerKills       9226 non-null   int64 \n",
      " 9   inhibitorKills   9226 non-null   int64 \n",
      " 10  baronKills       9226 non-null   int64 \n",
      " 11  dragonKills      9226 non-null   int64 \n",
      " 12  riftHeraldKills  9226 non-null   int64 \n",
      " 13  region           9226 non-null   object\n",
      "dtypes: int64(13), object(1)\n",
      "memory usage: 1009.2+ KB\n"
     ]
    }
   ],
   "source": [
    "match_df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x2e285b5c940>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set_style('darkgrid') #设置表风格，可选项包括{darkgrid, whitegrid, dark, white, ticks}\n",
    "plt.figure(figsize = (12,8)) #创建一个figure图形实例\n",
    "cmap = sns.diverging_palette(220, 20, l = 40, s = 99, sep = 20, center = 'light', as_cmap = True) #设置图像的colormap\n",
    "\n",
    "sns.heatmap(match_df.isna(), cmap = cmap, yticklabels = False) #利用df.isna()来获取是否存在nan并用热力图可视化"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "teamid            0.500001\n",
       "firstBlood        0.588848\n",
       "firstTower        0.702490\n",
       "firstInhibitor    0.913432\n",
       "firstBaron        0.802189\n",
       "dtype: float64"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#计算各个资源的获取对于游戏胜利的影响\n",
    "(match_df.groupby(['win']).sum().iloc[1]/(match_df.groupby(['win']).sum().iloc[1]+match_df.groupby(['win']).sum().iloc[0]))[:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x2e285bd0d30>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#设定图像样式\n",
    "plt.figure(figsize = (12,8))\n",
    "cmap = sns.diverging_palette(220, 20, l = 40, s = 99, sep = 20, center = 'light', as_cmap = True) \n",
    "\n",
    "#drop函数默认删除行，列需要加axis = 1\n",
    "#查看各个变量之间的相关系数\n",
    "sns.heatmap(match_df.drop(['region'], axis = 1).corr(), vmin = 0, vmax = 1, annot = True, cmap = cmap, lw = .5, linecolor = 'white')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x2e285cb1e48>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#同时我们也可以查看各地区的相关系数\n",
    "plt.figure(figsize = (12,8))\n",
    "sns.heatmap(match_df[match_df.region == 'kr'].drop(['region'], axis = 1).corr(), vmin = 0, vmax = 1, annot = True, cmap = cmap, lw = .5, linecolor = 'white')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# PCA部分"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "success\n"
     ]
    }
   ],
   "source": [
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.decomposition import PCA\n",
    "print('success')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[-1.73186308, -0.99913326, -0.99244131, ..., -0.62371444,\n",
       "        -1.15938506, -0.94860297],\n",
       "       [-1.73148761,  1.00086749,  1.00761626, ..., -0.62371444,\n",
       "         0.33255811, -0.94860297],\n",
       "       [-1.73111214, -0.99913326, -0.99244131, ..., -0.62371444,\n",
       "        -1.15938506, -0.94860297],\n",
       "       ...,\n",
       "       [ 1.73111214,  1.00086749, -0.99244131, ...,  1.08992148,\n",
       "         1.0785297 ,  0.40637628],\n",
       "       [ 1.73148761,  1.00086749,  1.00761626, ...,  2.8035574 ,\n",
       "         1.82450128, -0.94860297],\n",
       "       [ 1.73186308, -0.99913326, -0.99244131, ..., -0.62371444,\n",
       "        -0.41341347,  0.40637628]])"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#数据标准化 / 归一化\n",
    "scaler = StandardScaler()\n",
    "scaler.fit(match_df.drop(['win', 'region'], axis = 1))\n",
    "scaled_data = scaler.transform(match_df.drop(['win', 'region'], axis=1))\n",
    "scaled_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[0,\n",
       " 0.371132529193178,\n",
       " 0.5222827276244921,\n",
       " 0.6249863766909867,\n",
       " 0.7169140092860147,\n",
       " 0.7983414196662135,\n",
       " 0.8718622589889559,\n",
       " 0.9191467963276532]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "exp_var_ratio = []\n",
    "for n in range(0,8):\n",
    "    pca = PCA(n_components = n)\n",
    "    pca.fit(scaled_data)\n",
    "    pca.transform(scaled_data)\n",
    "    exp_var_ratio.append(sum(pca.explained_variance_ratio_))\n",
    "\n",
    "exp_var_ratio"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Ratio of Variance Explained')"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize = (12, 8))\n",
    "ax = fig.add_subplot(111)#添加一个画布用于绘图，“111”表示“1×1网格，第一子图”\n",
    "plt.plot(range(1,9), exp_var_ratio, marker = 'o', markerfacecolor = 'orange', markersize = 6)#绘制散点图\n",
    "#利用zip返回一个可迭代的对象\n",
    "for i,j in zip(range(1,9),exp_var_ratio):\n",
    "    ax.annotate('{:.2f}'.format(j),xy=(i-.2,j+.03))#标注图中的数据\n",
    "plt.xlabel('Number of Componenets', size = 15)\n",
    "plt.ylabel('Ratio of Variance Explained', size = 15)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "从上述PCA分析中我们可以看到，在六个变量的情况下可解释度来到了80%。这样我们可以可以选择6个对应特征来进行后续分析"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x2e286dfe438>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#将PCA特征的个数选定在6个\n",
    "pca = PCA(n_components = 6)\n",
    "pca.fit(scaled_data)\n",
    "data_pca = pca.transform(scaled_data)\n",
    "\n",
    "pca_corr = pd.DataFrame(pca.components_, columns = match_df.drop(['win', 'region'], axis = 1).columns)#pca.components_:返回具有最大方差的成分\n",
    "\n",
    "plt.figure(figsize = (12,8))\n",
    "sns.heatmap(pca_corr, cmap = cmap, vmin = -1, vmax = 1, annot = True, lw = .5, linecolor = 'white')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.37113253, 0.1511502 , 0.10270365, 0.09192763, 0.08142741,\n",
       "       0.07352084])"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#explained_variance_ratio_，它代表降维后的各主成分的方差值占总方差值的比例，这个比例越大，则越是重要的主成分。\n",
    "pca.explained_variance_ratio_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([4.45407312, 1.813999  , 1.23257739, 1.10325117, 0.97723485,\n",
       "       0.88234571])"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#explained_variance_，它代表降维后的各主成分的方差值，方差值越大，则说明越是重要的主成分。\n",
    "pca.explained_variance_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 逻辑回归部分（Logistic Regression）"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.metrics import classification_report, confusion_matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.83      0.88      0.86      1381\n",
      "           1       0.88      0.83      0.85      1387\n",
      "\n",
      "    accuracy                           0.85      2768\n",
      "   macro avg       0.86      0.85      0.85      2768\n",
      "weighted avg       0.86      0.85      0.85      2768\n",
      "\n",
      "[[1219  162]\n",
      " [ 241 1146]]\n",
      "teamid            -0.000036\n",
      "firstBlood         0.344649\n",
      "firstTower         0.932013\n",
      "firstInhibitor     1.451327\n",
      "firstBaron         0.158178\n",
      "firstDragon        0.324746\n",
      "firstRiftHerald    0.106132\n",
      "towerKills         0.492882\n",
      "inhibitorKills     0.079540\n",
      "baronKills        -0.312729\n",
      "dragonKills        0.059923\n",
      "riftHeraldKills   -0.292239\n",
      "dtype: float64\n",
      "[[-3.63454052e-05  3.44648562e-01  9.32013327e-01  1.45132708e+00\n",
      "   1.58177551e-01  3.24745615e-01  1.06132142e-01  4.92881742e-01\n",
      "   7.95404302e-02 -3.12728980e-01  5.99226511e-02 -2.92238525e-01]]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\kevin.shu\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n"
     ]
    }
   ],
   "source": [
    "X = match_df.drop(['win', 'region'], axis = 1)\n",
    "y = match_df.win\n",
    "\n",
    "#使用train_test_split将数据集分为训练街和测试集\n",
    "#train_data：所要划分的样本特征集  train_target：所要划分的样本结果  test_size：样本占比，如果是整数的话就是样本的数量\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.3)\n",
    "\n",
    "log = LogisticRegression()\n",
    "log.fit(X_train, y_train)\n",
    "y_pred = log.predict(X_test)#predict方法返回分类结果的预测，注意和predict_proba方法区分并灵活使用\n",
    "\n",
    "#输出分类报告\n",
    "#其中列表左边的一列为分类的标签名，右边support列为每个标签的出现次数．avg / total行为各列的均值（support列为总和）． \n",
    "#precision recall f1-score三列分别为各个类别的精确度,召回率及 F1值．\n",
    "print(classification_report(y_test, y_pred))\n",
    "\n",
    "#输出混淆矩阵\n",
    "print(confusion_matrix(y_test, y_pred))\n",
    "\n",
    "#对于输出后的模型，输出coef_（模型系数）.intercept_为截距\n",
    "log_coeff_tot = pd.Series(log.coef_[0], index = match_df.drop(['win', 'region'], axis = 1).columns)\n",
    "print(log_coeff_tot)\n",
    "print(log.coef_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.86      0.89      0.88       308\n",
      "           1       0.87      0.84      0.86       274\n",
      "\n",
      "    accuracy                           0.87       582\n",
      "   macro avg       0.87      0.86      0.87       582\n",
      "weighted avg       0.87      0.87      0.87       582\n",
      "\n",
      "[[274  34]\n",
      " [ 44 230]]\n",
      "\n",
      "teamid             0.000046\n",
      "firstBlood         0.444205\n",
      "firstTower         1.190063\n",
      "firstInhibitor     1.321729\n",
      "firstBaron         0.736395\n",
      "firstDragon        0.691792\n",
      "firstRiftHerald   -0.025646\n",
      "towerKills         0.371196\n",
      "inhibitorKills     0.667583\n",
      "baronKills        -0.750966\n",
      "dragonKills        0.249514\n",
      "riftHeraldKills   -0.201459\n",
      "dtype: float64\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\kevin.shu\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n"
     ]
    }
   ],
   "source": [
    "#na地区的分析\n",
    "X = match_df[match_df.region == 'na1'].drop(['win', 'region'], axis = 1)\n",
    "y = match_df[match_df.region == 'na1'].win\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = .3)\n",
    "\n",
    "log = LogisticRegression()\n",
    "log.fit(X_train, y_train)\n",
    "y_pred = log.predict(X_test)\n",
    "\n",
    "print(classification_report(y_test, y_pred))\n",
    "print(confusion_matrix(y_test, y_pred))\n",
    "print()\n",
    "\n",
    "log_coeff_na = pd.Series(log.coef_[0], index = match_df.drop(['win', 'region'], axis = 1).columns)\n",
    "print(log_coeff_na)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\kevin.shu\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.86      0.83      0.84       283\n",
      "           1       0.83      0.86      0.84       272\n",
      "\n",
      "    accuracy                           0.84       555\n",
      "   macro avg       0.84      0.84      0.84       555\n",
      "weighted avg       0.84      0.84      0.84       555\n",
      "\n",
      "[[235  48]\n",
      " [ 39 233]]\n",
      "\n",
      "teamid            -0.000068\n",
      "firstBlood         0.350687\n",
      "firstTower         0.890266\n",
      "firstInhibitor     1.150908\n",
      "firstBaron         0.194500\n",
      "firstDragon        0.252164\n",
      "firstRiftHerald    0.370165\n",
      "towerKills         0.390492\n",
      "inhibitorKills     0.479636\n",
      "baronKills        -0.239031\n",
      "dragonKills        0.060723\n",
      "riftHeraldKills   -0.309682\n",
      "dtype: float64\n"
     ]
    }
   ],
   "source": [
    "#br地区分析\n",
    "X = match_df[match_df.region == 'br1'].drop(['win', 'region'], axis = 1)\n",
    "y = match_df[match_df.region == 'br1'].win\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = .3)\n",
    "\n",
    "log = LogisticRegression()\n",
    "log.fit(X_train, y_train)\n",
    "y_pred = log.predict(X_test)\n",
    "\n",
    "print(classification_report(y_test, y_pred))\n",
    "print(confusion_matrix(y_test, y_pred))\n",
    "print()\n",
    "\n",
    "log_coeff_br = pd.Series(log.coef_[0], index = match_df.drop(['win', 'region'], axis = 1).columns)\n",
    "print(log_coeff_br)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.81      0.88      0.84       279\n",
      "           1       0.89      0.82      0.85       320\n",
      "\n",
      "    accuracy                           0.85       599\n",
      "   macro avg       0.85      0.85      0.85       599\n",
      "weighted avg       0.85      0.85      0.85       599\n",
      "\n",
      "[[245  34]\n",
      " [ 58 262]]\n",
      "\n",
      "teamid            -0.000127\n",
      "firstBlood         0.499851\n",
      "firstTower         0.933643\n",
      "firstInhibitor     0.815675\n",
      "firstBaron        -0.409971\n",
      "firstDragon        0.209068\n",
      "firstRiftHerald    0.249128\n",
      "towerKills         0.400673\n",
      "inhibitorKills     0.438176\n",
      "baronKills        -0.127218\n",
      "dragonKills        0.382233\n",
      "riftHeraldKills   -0.280716\n",
      "dtype: float64\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\kevin.shu\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n"
     ]
    }
   ],
   "source": [
    "#kr地区分析\n",
    "X = match_df[match_df.region == 'kr'].drop(['win', 'region'], axis = 1)\n",
    "y = match_df[match_df.region == 'kr'].win\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = .3)\n",
    "\n",
    "log = LogisticRegression()\n",
    "log.fit(X_train, y_train)\n",
    "y_pred = log.predict(X_test)\n",
    "\n",
    "print(classification_report(y_test, y_pred))\n",
    "print(confusion_matrix(y_test, y_pred))\n",
    "print()\n",
    "\n",
    "log_coeff_kr = pd.Series(log.coef_[0], index = match_df.drop(['win', 'region'], axis = 1).columns)\n",
    "print(log_coeff_kr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.83      0.87      0.85       245\n",
      "           1       0.87      0.82      0.85       257\n",
      "\n",
      "    accuracy                           0.85       502\n",
      "   macro avg       0.85      0.85      0.85       502\n",
      "weighted avg       0.85      0.85      0.85       502\n",
      "\n",
      "[[213  32]\n",
      " [ 45 212]]\n",
      "\n",
      "teamid            -0.000406\n",
      "firstBlood         0.716799\n",
      "firstTower         0.813798\n",
      "firstInhibitor     2.048290\n",
      "firstBaron         1.231427\n",
      "firstDragon       -0.115235\n",
      "firstRiftHerald   -0.003459\n",
      "towerKills         0.589678\n",
      "inhibitorKills    -0.318775\n",
      "baronKills        -0.861175\n",
      "dragonKills       -0.084986\n",
      "riftHeraldKills   -0.388769\n",
      "dtype: float64\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\kevin.shu\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n"
     ]
    }
   ],
   "source": [
    "#euw地区分析\n",
    "X = match_df[match_df.region == 'euw1'].drop(['win', 'region'], axis = 1)\n",
    "y = match_df[match_df.region == 'euw1'].win\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = .3)\n",
    "\n",
    "log = LogisticRegression()\n",
    "log.fit(X_train, y_train)\n",
    "y_pred = log.predict(X_test)\n",
    "\n",
    "print(classification_report(y_test, y_pred))\n",
    "print(confusion_matrix(y_test, y_pred))\n",
    "print()\n",
    "\n",
    "log_coeff_euw = pd.Series(log.coef_[0], index = match_df.drop(['win', 'region'], axis = 1).columns)\n",
    "print(log_coeff_euw)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.84      0.90      0.87       276\n",
      "           1       0.89      0.82      0.85       256\n",
      "\n",
      "    accuracy                           0.86       532\n",
      "   macro avg       0.86      0.86      0.86       532\n",
      "weighted avg       0.86      0.86      0.86       532\n",
      "\n",
      "[[249  27]\n",
      " [ 47 209]]\n",
      "\n",
      "teamid            -0.000274\n",
      "firstBlood         0.181978\n",
      "firstTower         0.547007\n",
      "firstInhibitor     1.511561\n",
      "firstBaron        -0.207177\n",
      "firstDragon        0.405552\n",
      "firstRiftHerald   -0.754657\n",
      "towerKills         0.517735\n",
      "inhibitorKills     0.003671\n",
      "baronKills         0.301555\n",
      "dragonKills       -0.170810\n",
      "riftHeraldKills    0.166387\n",
      "dtype: float64\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\kevin.shu\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n"
     ]
    }
   ],
   "source": [
    "#eun地区分析\n",
    "X = match_df[match_df.region == 'eun1'].drop(['win', 'region'], axis = 1)\n",
    "y = match_df[match_df.region == 'eun1'].win\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = .3)\n",
    "\n",
    "log = LogisticRegression()\n",
    "log.fit(X_train, y_train)\n",
    "y_pred = log.predict(X_test)\n",
    "\n",
    "print(classification_report(y_test, y_pred))\n",
    "print(confusion_matrix(y_test, y_pred))\n",
    "print()\n",
    "\n",
    "log_coeff_eun = pd.Series(log.coef_[0], index = match_df.drop(['win', 'region'], axis = 1).columns)\n",
    "print(log_coeff_eun)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>overall</th>\n",
       "      <th>na</th>\n",
       "      <th>br</th>\n",
       "      <th>kr</th>\n",
       "      <th>euw</th>\n",
       "      <th>eun</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>teamid</th>\n",
       "      <td>-0.000036</td>\n",
       "      <td>0.000046</td>\n",
       "      <td>-0.000068</td>\n",
       "      <td>-0.000127</td>\n",
       "      <td>-0.000406</td>\n",
       "      <td>-0.000274</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>firstBlood</th>\n",
       "      <td>0.344649</td>\n",
       "      <td>0.444205</td>\n",
       "      <td>0.350687</td>\n",
       "      <td>0.499851</td>\n",
       "      <td>0.716799</td>\n",
       "      <td>0.181978</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>firstTower</th>\n",
       "      <td>0.932013</td>\n",
       "      <td>1.190063</td>\n",
       "      <td>0.890266</td>\n",
       "      <td>0.933643</td>\n",
       "      <td>0.813798</td>\n",
       "      <td>0.547007</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>firstInhibitor</th>\n",
       "      <td>1.451327</td>\n",
       "      <td>1.321729</td>\n",
       "      <td>1.150908</td>\n",
       "      <td>0.815675</td>\n",
       "      <td>2.048290</td>\n",
       "      <td>1.511561</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>firstBaron</th>\n",
       "      <td>0.158178</td>\n",
       "      <td>0.736395</td>\n",
       "      <td>0.194500</td>\n",
       "      <td>-0.409971</td>\n",
       "      <td>1.231427</td>\n",
       "      <td>-0.207177</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>firstDragon</th>\n",
       "      <td>0.324746</td>\n",
       "      <td>0.691792</td>\n",
       "      <td>0.252164</td>\n",
       "      <td>0.209068</td>\n",
       "      <td>-0.115235</td>\n",
       "      <td>0.405552</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>firstRiftHerald</th>\n",
       "      <td>0.106132</td>\n",
       "      <td>-0.025646</td>\n",
       "      <td>0.370165</td>\n",
       "      <td>0.249128</td>\n",
       "      <td>-0.003459</td>\n",
       "      <td>-0.754657</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>towerKills</th>\n",
       "      <td>0.492882</td>\n",
       "      <td>0.371196</td>\n",
       "      <td>0.390492</td>\n",
       "      <td>0.400673</td>\n",
       "      <td>0.589678</td>\n",
       "      <td>0.517735</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>inhibitorKills</th>\n",
       "      <td>0.079540</td>\n",
       "      <td>0.667583</td>\n",
       "      <td>0.479636</td>\n",
       "      <td>0.438176</td>\n",
       "      <td>-0.318775</td>\n",
       "      <td>0.003671</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>baronKills</th>\n",
       "      <td>-0.312729</td>\n",
       "      <td>-0.750966</td>\n",
       "      <td>-0.239031</td>\n",
       "      <td>-0.127218</td>\n",
       "      <td>-0.861175</td>\n",
       "      <td>0.301555</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>dragonKills</th>\n",
       "      <td>0.059923</td>\n",
       "      <td>0.249514</td>\n",
       "      <td>0.060723</td>\n",
       "      <td>0.382233</td>\n",
       "      <td>-0.084986</td>\n",
       "      <td>-0.170810</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>riftHeraldKills</th>\n",
       "      <td>-0.292239</td>\n",
       "      <td>-0.201459</td>\n",
       "      <td>-0.309682</td>\n",
       "      <td>-0.280716</td>\n",
       "      <td>-0.388769</td>\n",
       "      <td>0.166387</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  overall        na        br        kr       euw       eun\n",
       "teamid          -0.000036  0.000046 -0.000068 -0.000127 -0.000406 -0.000274\n",
       "firstBlood       0.344649  0.444205  0.350687  0.499851  0.716799  0.181978\n",
       "firstTower       0.932013  1.190063  0.890266  0.933643  0.813798  0.547007\n",
       "firstInhibitor   1.451327  1.321729  1.150908  0.815675  2.048290  1.511561\n",
       "firstBaron       0.158178  0.736395  0.194500 -0.409971  1.231427 -0.207177\n",
       "firstDragon      0.324746  0.691792  0.252164  0.209068 -0.115235  0.405552\n",
       "firstRiftHerald  0.106132 -0.025646  0.370165  0.249128 -0.003459 -0.754657\n",
       "towerKills       0.492882  0.371196  0.390492  0.400673  0.589678  0.517735\n",
       "inhibitorKills   0.079540  0.667583  0.479636  0.438176 -0.318775  0.003671\n",
       "baronKills      -0.312729 -0.750966 -0.239031 -0.127218 -0.861175  0.301555\n",
       "dragonKills      0.059923  0.249514  0.060723  0.382233 -0.084986 -0.170810\n",
       "riftHeraldKills -0.292239 -0.201459 -0.309682 -0.280716 -0.388769  0.166387"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#各地区的权重差异\n",
    "win_conditions_dict = dict()\n",
    "win_conditions_dict['overall'] = log_coeff_tot\n",
    "win_conditions_dict['na'] = log_coeff_na\n",
    "win_conditions_dict['br'] = log_coeff_br\n",
    "win_conditions_dict['kr'] = log_coeff_kr\n",
    "win_conditions_dict['euw'] = log_coeff_euw\n",
    "win_conditions_dict['eun'] = log_coeff_eun\n",
    "win_conditions = pd.DataFrame(win_conditions_dict)\n",
    "win_conditions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x2e28cda5198>"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#各地区的权重差异可视化\n",
    "plt.style.use('fivethirtyeight')\n",
    "win_conditions.plot(kind = 'bar', figsize = (12, 8))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
